


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase     :							Optional[Any]    =				{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							Optional[int]    =				["""ConvBertTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							int    =				[
		    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ConvBertForMaskedLM""",
		    """ConvBertForMultipleChoice""",
		    """ConvBertForQuestionAnswering""",
		    """ConvBertForSequenceClassification""",
		    """ConvBertForTokenClassification""",
		    """ConvBertLayer""",
		    """ConvBertModel""",
		    """ConvBertPreTrainedModel""",
		    """load_tf_weights_in_convbert""",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							Any    =				[
		    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFConvBertForMaskedLM""",
		    """TFConvBertForMultipleChoice""",
		    """TFConvBertForQuestionAnswering""",
		    """TFConvBertForSequenceClassification""",
		    """TFConvBertForTokenClassification""",
		    """TFConvBertLayer""",
		    """TFConvBertModel""",
		    """TFConvBertPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

		_lowerCAmelCase     :							List[Any]    =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



